1,758 research outputs found

    Investigating The Relationship Between Adverse Events And Infrastructure Development In An Active War Theater Using Soft Computing Techniques

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    The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events

    Data Stream Models for Predicting Adverse Events in a War Theater

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    Predicting adverse events in a war theater has been an active area of research. Recent studies used machine learning methods to predict adverse events utilizing infrastructure development spending data as input variables. The goals of these studies were to find correlation and disclose the main factors between adverse events and human-social-infrastructure development projects, and reduce the occurrence of the adverse events. The predictions still have large errors compared with the real values using the existing methods. The reason could be that some significant variables are removed to comply with constraints in a soft computing model such as neural networks, fuzzy inference systems (FIS) and adaptive neuro-fuzzy inference systems (ANFIS) that work well with a smaller number of variables. In this paper, a data stream approach using three data stream regression algorithms, AMRules, TargetMean and FIMTDD, is proposed to predict the adverse events so that much more input variables could be included. The results show that the data stream methods generate better results than machine learning methods used in the previous studies, thus helping us better understand the relationship between infrastructure development and adverse events. In addition the data stream methods also outperform the traditional linear regression model. An important advantage in using data stream methods is the ability to create and apply predictive models with a relatively small amount of memory and time. Finally, the use of data stream methods provides an additional advantage by allowing the user to observe error distribution over time for more accurate assessment of the performance of the resulting models

    2023- The Twenty-seventh Annual Symposium of Student Scholars

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    The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

    Reports to the President

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    A compilation of annual reports for the 1999-2000 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

    Crossbow Volume 1

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    Student Integrated ProjectIncludes supplementary materialDistributing naval combat power into many small ships and unmanned air vehicles that capitalize on emerging technology offers a transformational way to think about naval combat in the littorals in the 2020 time frame. Project CROSSBOW is an engineered systems of systems that proposes to use such distributed forces to provide forward presence to gain and maiantain access, to provide sea control, and to project combat power in the littoral regions of the world. Project CROSSBOW is the result of a yearlong, campus-wide, integrated research systems engineering effort involving 40 student researchers and 15 supervising faculty members. This report (Volume I) summarizes the CROSSBOW project. It catalogs the major features of each of the components, and includes by reference a separate volume for each of the major systems (ships, aircraft, and logistics). It also prresents the results of the mission and campaign analysis that informed the trade-offs between these components. It describes certain functions of CROSSBOW in detail through specialized supporting studies. The student work presented here is technologically feasible, integrated and imaginative. The student project cannot by itself provide definitive designs or analyses covering such a broad topic. It does strongly suggest that the underlying concepts have merit and deserve further serious study by the Navy as it transforms itself

    2014 Abstracts Student Research Conference

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    Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

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    The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969

    Mission Assurance: A Review of Continuity of Operations Guidance for Application to Cyber Incident Mission Impact Assessment (CIMIA)

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    Military organizations have embedded information technology (IT) into their core mission processes as a means to increase operational efficiency, improve decision-making quality, and shorten the sensor-to-shooter cycle. This IT-to-mission dependence can place the organizational mission at risk when an information incident (e.g., the loss or manipulation of a critical information resource) occurs. Non-military organizations typically address this type of IT risk through an introspective, enterprise-wide focused risk management program that continuously identifies, prioritizes, and documents risks so an economical set of control measures (e.g., people, processes, technology) can be selected to mitigate the risks to an acceptable level. The explicit valuation of information resources in terms of their ability to support the organizational mission objectives provides transparency and enables the creation of a continuity of operations plan and an incident recovery plan. While this type of planning has proven successful in static environments, military missions often involve dynamically changing, time-sensitive, complex, coordinated operations involving multiple organizational entities. As a consequence, risk mitigation efforts tend to be localized to each organizational entity making the enterprise-wide risk management approach to mission assurance infeasible. This thesis investigates the concept of mission assurance and presents a content analysis of existing continuity of operations elements within military and non-military guidance to assess the current policy landscape to highlight best practices and identify policy gaps in an effort to further enhance mission assurance by improving the timeliness and relevance of notification following an information incident
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